320 Bridging the gap: Effective promotion of academic and community engaged (PACE) research dissemination strategies
Bibliographic record
Abstract
Objectives/Goals: Present a framework for hosting Community Grand Rounds, where community and academic partners showcase completed community-engaged research (CEnR) projects. This highlights innovative dissemination methods, engages diverse audiences, elicits community responses, and advances the translational science of CEnR. Methods/Study Population: Our approach involves planning and outreach to collaborate with promotion of academic and community engaged grantees to develop community dissemination events that translate the science of CE into accessible, relatable, culturally relevant formats for diverse audiences. These events incorporate interactive presentations that encourage active participation and feedback from attendees. Following each event, an evaluation is completed to assess community impact. Key strategies for hosting, facilitating, and utilizing diverse marketing to ensure that events are tailored to culturally diverse community groups, including regional implementation when practical. This collaborative approach meets a critical need and strengthens the bond between researchers and the communities they aim to serve. Results/Anticipated Results: These events create a feedback loop between the community and academic researchers. It was not just about telling people what was found. We created opportunities for community members and academics to build trust, give us feedback, ask questions, and discuss how findings could be practically applied. By presenting the findings in an accessible way within the community, community members are more informed and empowered to make decisions or advocate for changes in their own lives based on the research. Academics also benefited from community feedback, which provided new insights to help refine future research questions and methods. The goal is for shared conversation and understanding between community members and academics to inspire real-world applications and policy change directly informed by the research. Discussion/Significance of Impact: Community Grand Rounds are one dissemination strategy to leverage community–academic collaboration to present tailored research, fostering engagement, understanding, and action between researchers and community members. This approach effectively enhances the translational science of CEnR by involving and benefiting the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".